The ammonium nitrate (AN) and fuel oil (FO) mixture known as ANFO is a typical representative of non-ideal explosives. In contrast to ideal explosives, the detonation behavior of ANFO exhibits a strong dependence on charge diameter, existence, and properties of confinement, with a large failure diameter and long distance required to establish steady-state detonation. In this study shock initiation and propagation of detonation in ANFO were studied experimentally by determining the detonation velocity at different distances from the initiation point, as well as by numerical modeling using AUTODYN hydrodynamics code and a Wood–Kirkwood detonation model incorporated into EXPLO5 thermochemical code. The run-to-steady-state detonation velocity distance was determined as a function of charge diameter, booster charge mass, and confinement. It was demonstrated that a Lee–Tarver ignition and growth reactive flow model with properly calibrated rate constants was capable of correctly ascertaining experimentally observed shock initiation behavior and propagation of detonation in ANFO, as well as the effects of charge diameter, booster mass, and confinement.

The Goldeneye, i.e. ‘Earth observation (EO) and Earth global navigation satellite system (GNSS) data acquisition and processing platform for safe, sustainable and cost-efficient mining operations’, project implements a unique combination of remote sensing and positioning technologies, exploiting EO and Earth GNSS data, together with data fusion and processing powered by data analytics and machine-learning algorithms. The aim of this paper is to evaluate the impact provided by seven techniques and methods for the five mine sites in the Goldeneye project. The emphasis is to analyse and reflect how, and with what focus areas, the developed analytical capabilities meet the needs of different mine sites that collaborated in the project in piloting and testing. The results show that the outcomes of this research and development project hold immense significance for the mining industry, as they contribute to its transformation, digitalization and twin transition. This contribution is realized through the broad adoption of sensing and artificial-intelligence-based applications, solutions and services within the mining industry. This benefits not only the companies directly involved but also the entire value chain and related industries, and contributes to the European Union level and national goals in the mining sector.

Availability of large-scale geospatial datasets, developments of effective algorithms and access to powerful computing resources have resulted in the unprecedented quantities of data being generated in recent years. This vast volume of available Earth observation data has triggered the need to find new ways to exploit its full potential. The GOLDENAI platform was developed to maximize geospatial data potential specifically for mineral exploration and mining. The platform's innovative component-based architecture consists of a back-end for data acquisition, processing, artificial intelligence (AI)-driven analysis, and a user-friendly front-end for interactive data exploration. Uniquely tailored for the mining sector, the platform integrates automated AI knowledge packs to streamline processes such as soil moisture time-series analysis, principal component analysis for mineral mapping, spectral indices, RGB composites and unsupervised clustering for geospatial analysis. Throughout the lifecycle of the Goldeneye project and through real-world field trials in the Golden Eye project, we demonstrate how GOLDENAI significantly enhances processing efficiency. This robust and scalable platform, accessible via a web-based interface, simplifies complex data workflows, making it a comprehensive and valuable tool for industry and academic stakeholders in mineral resource management.